machine learning techniques, which tend to be assumption-free ML should be a rigorous exercise in Bayesian and classical/frequentist stats, computational meth

machine learning techniques, which tend to be assumption-free ML should be a rigorous exercise in Bayesian and classical/frequentist stats, computational methods, dataset integrity, visualization etc, if you've been thru the texts by Murphy or Bishop. It often happens that people a couple years out of their last stats class only retain that high R-squared, p-, t- and f-values are what they're looking for, and heteroskedasticity and sphericity are just big words. My evidence that ML is a rigorous exercise: the free texts listed (Barber, Mackay and Smola's are excellent, ESL not as accessible) http://metaoptimize.com/qa/questions/186/good-freely-available-textbooks-on-machine-learning http://metaoptimize.com/qa/questions/186/good-freely-availab...